Queueing Theory and Network Applications : 13th International Conference, QTNA 2018, Tsukuba, Japan, July 25-27, 2018, Proceedings

Yutaka Takahashi

ISBN 10: 3319937359 / ISBN 13: 9783319937359

Edité par Springer International Publishing, Springer International Publishing, 2018

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the proceedings of the 13th International Conference on Queueing Theory and Network Applications, QTNA 2018, held in Tsukuba, Japan in July 2018.The 8 full papers together with 10 short papers included in this volume were carefully reviewed and selected from 57 initial submissions.All the papers to be presented disseminate the latest results covering up-to-date research fields such as performance modeling and analysis of telecommunication systems, retrial and vacation queueing models, optimization of queueing systems, modeling of social systems, application of machine learning in queueing models.

N° de réf. du vendeur 9783319937359

Détails bibliographiques

• Titre: Queueing Theory and Network Applications : 13th International Conference, QTNA 2018, Tsukuba, Japan, July 25-27, 2018, Proceedings
• Éditeur: Springer International Publishing, Springer International Publishing
• Date de parution: 2018
• Langue: anglais
• ISBN à 10 chiffres: 3319937359
• ISBN à 13 chiffres: 9783319937359
• Reliure: Taschenbuch
• État de l'article: Neu

A propos de ce titre

Synopsis

This book constitutes the proceedings of the 13th International Conference on Queueing Theory and Network Applications, QTNA 2018, held in Tsukuba, Japan in July 2018.

The 8 full papers together with 10 short papers included in this volume were carefully reviewed and selected from 57 initial submissions. All the papers to be presented disseminate the latest results covering up-to-date research fields such as performance modeling and analysis of telecommunication systems, retrial and vacation queueing models, optimization of queueing systems, modeling of social systems, application of machine learning in queueing models.